Search results

Taking the lens of a cue diagnosticity framework and affective primacy theory, this study aims to examine the relative effects of cognitive and affective country image on consumer cognitive judgement, affective evaluation and behavioural tendency in one integrated model. It also explores how the direct effects may vary with the intra-valence nature (ambivalent vs. univalent) of cognition-affect.

The proposed research model was tested using data from a large Chinese sample and consumer responses to products from four countries − the USA, Japan, Brazil and India.

The results show that the relative effects of cognitive and affective country image are complex and differ by the intra-valence nature of cognition-affect. On a general level, cognitive and affective country image exert equal influence on affective evaluation and behavioural tendency. In contrast, cognitive country image demonstrates a more prominent effect than affective country image on cognitive judgement. Compared with univalent, ambivalent cognition-affect strengthens the positive impact of affective country image but does not significantly alter the positive impact of cognitive country image on consumer reactions.

This research contributes to the ongoing debate regarding implications of two focal aspects of macro country image by revealing their relative importance in an integrated framework and enriches country-of-origin research through unveiling the uni/ambivalent cognition-affect as a moderator of the relationship between cognitive/affective country image and consumer reactions. The research findings provide implications as to whether and when marketing strategies should focus on leveraging positive (negative) cognitive or affective country image.

This paper aims to discuss recent criticism related to partial least squares structural equation modeling (PLS-SEM).

Using a combination of literature reviews, empirical examples, and simulation evidence, this research demonstrates that critical accounts of PLS-SEM paint an overly negative picture of PLS-SEM’s capabilities.

Criticisms of PLS-SEM often generalize from boundary conditions with little practical relevance to the method’s general performance, and disregard the metrics and analyses (e.g., Type I error assessment) that are important when assessing the method’s efficacy.

We believe the alleged “fallacies” and “untold facts” have already been addressed in prior research and that the discussion should shift toward constructive avenues by exploring future research areas that are relevant to PLS-SEM applications.

All statistical methods, including PLS-SEM, have strengths and weaknesses. Researchers need to consider established guidelines and recent advancements when using the method, especially given the fast pace of developments in the field.

This research addresses criticisms of PLS-SEM and offers researchers, reviewers, and journal editors a more constructive view of its capabilities.

This study aims to understand and a lay a foundation of how analytics has been used in depression management, this study conducts a systematic literature review using two techniques – text mining and manual review. The proposed methodology would aid researchers in identifying key concepts and research gaps, which in turn, will help them to establish the theoretical background supporting their empirical research objective.

This paper explores a hybrid methodology for literature review (HMLR), using text mining prior to systematic manual review.

The proposed rapid methodology is an effective tool to automate and speed up the process required to identify key and emerging concepts and research gaps in any specific research domain while conducting a systematic literature review. It assists in populating a research knowledge graph that does not reach all semantic depths of the examined domain yet provides some science-specific structure.

This study presents a new methodology for conducting a literature review for empirical research articles. This study has explored an “HMLR” that combines text mining and manual systematic literature review. Depending on the purpose of the research, these two techniques can be used in tandem to undertake a comprehensive literature review, by combining pieces of complex textual data together and revealing areas where research might be lacking.

Accurate diagnosis of brain tumors is crucial for effective treatment and improved patient outcomes. Magnetic resonance imaging (MRI) is a common method for detecting brain malignancies, but interpreting MRI data can be challenging and time-consuming for healthcare professionals.

An innovative method is presented that combines deep learning (DL) models with natural language processing (NLP) from ChatGPT to enhance the accuracy of brain tumor detection in MRI scans. The method generates textual descriptions of brain tumor regions, providing clinicians with valuable insights into tumor characteristics for informed decision-making and personalized treatment planning.

The evaluation of this approach demonstrates promising outcomes, achieving a notable Dice coefficient score of 0.93 for tumor segmentation, outperforming current state-of-the-art methods. Human validation of the generated descriptions confirms their precision and conciseness.

While the method showcased advancements in accuracy and understandability, ongoing research is essential for refining the model and addressing limitations in segmenting smaller or atypical tumors.

These results emphasized the potential of this innovative method in advancing neuroimaging practices and contributing to the effective detection and management of brain tumors.

There is an emerging interest in understanding the cognitive, emotional and motivational processes that drive tourists' behaviour using neuroscientific research methods. This chapter briefly reviews the main methods of interest to tourism researchers, to then focuses on electroencephalography, which reflects electrical activity from the brain. Event-related potentials or electroencephalography oscillations reflect cognitive and affective processes. Components of the former can index emotional brain responses, and alpha oscillations are related to attention and approach/withdrawal. Existing tourism literature/using electroencephalography are reviewed. This is a promising tool for studying a range of phenomena that are of interest to tourism scholars, but require careful use of methods and interpretation.

This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.

Multiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.

The study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.

This study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

The objective is to identify the effects of suspicion as well as knowledge gaps, especially in noninterpersonal contexts. This study aims to propose a robust framework for future research. The overarching goal is to foster a comprehensive understanding of consumer suspicion, its implications and its potential avenues in the ever-evolving field of consumer behavior.

Based on a focused review of the literature, this study synthesizes the effects of suspicion in interpersonal and noninterpersonal contexts to unveil its importance for consumer behavior.

The cognitive, affective and behavioral effects of suspicion are identified. Furthermore, a discernible imbalance is observed, as the predominant focus on interpersonal consumer contexts leaves a significant gap in the comprehension of how consumers navigate and perceive suspicion in noninterpersonal interactions. This topic is important especially in an era dominated by complex brand interrelationships and digital touchpoints. Also, the operationalization of the suspicion construct in a plethora of studies seems to be suboptimal, suggesting a need for improvements with respect to its dynamic nature. In this regard, this review provides insightful directions to advance research in the abovementioned domains.

The synthesis of the findings of the empirical articles did not focus on variations in consumer suspicion across different cultures or regions. In addition, the dynamic nature of suspicion and the evolving landscape of consumer behavior mean that findings and implications may require periodic reassessment to maintain relevance. Also, this review did not delve into the methodological diversities across the studies examined.

This review offers marketers and businesses critical insights into the consumer suspicion dynamics. By understanding these nuances, companies can tailor strategies to mitigate suspicion and optimize consumer relationships.

Through synthesizing the effects of suspicion and providing avenues for future research, this study significantly contributes to consumer behavior literature.

In this study, the research question posed was: What are the defining characteristics, limitations, and potential opportunities in the research on heterogeneity within ASD?

This scoping review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to address the research question: “What are the defining characteristics, limitations, and potential opportunities in the research on heterogeneity within ASD?” A comprehensive literature search was conducted across databases including MEDLINE/PubMed, SciVerse Scopus and Springer Link, with keywords such as autism, autism spectrum disorder (ASD), heterogeneity and neurodevelopment. Inclusion criteria covered original research, reviews and protocols published since 1990, while irrelevant or out-of-date works were excluded. Thematic analysis was applied to collected data to identify common patterns, trends and key characteristics, leading to a narrative synthesis. Ethical review board approval was not required due to the nature of the review.

The scoping review underscored the multifaceted nature of ASD, emphasizing its clinical, methodological and investigational complexities. ASD’s diverse behavioral, social and biological characteristics challenged its classification as a uniform entity. To address this, the review examined strategies like stricter clinical criteria, categorization into functional subgroups, and larger, diverse sample sizes. Moreover, it highlighted the transformative role of Big Data and machine learning in advancing the comprehension of ASD’s manifold manifestations. This research contributed valuable insights and innovative approaches for addressing the intrinsic heterogeneity of ASD, reshaping the understanding of this complex condition.

One limitation of this scoping review is that it primarily relied on existing literature and did not involve primary data collection. While the review synthesized and analyzed a substantial body of research, the absence of original data collection may limit the depth of insights into specific aspects of ASD heterogeneity. Future research could benefit from incorporating primary data collection methods, such as surveys or interviews with individuals with ASD and their families, to gain more nuanced perspectives on the condition’s heterogeneity.

The reliance on existing literature in this scoping review highlights the need for further empirical studies exploring ASD’s heterogeneity. Researchers should consider conducting primary data collection to capture real-world experiences and variations within the ASD population. This approach could provide more comprehensive and context-specific insights, ultimately informing the development of tailored interventions and support strategies for individuals with ASD and their families.

This paper offers a fresh perspective on understanding ASD by examining its clinical, methodological and investigational implications in light of its inherent heterogeneity. Rather than viewing ASD as a uniform condition, this study explores strategies such as stricter clinical criteria, subcategorization based on functionality and diverse sample sizes to address its complexity. In addition, this study highlights the innovative use of Big Data and machine learning to gain deeper insights into ASD’s diverse manifestations. This approach contributes new insights and promising directions for future research, challenging the conventional understanding of ASD as a singular entity.